Automatic text recognition in natural scene and its translation into user defined language

In recent year's availability of economical image capturing devices in low cost products like mobile phones has led a significant attention of researchers to the problem of recognizing text in images. Recognition of scene text is a challenging problem compared to the recognition of printed documents. In this work a novel approach is proposed to recognize text in complex background natural scene, word formation from recognized text, spelling checking and word translation into user defined language and finally overlay translated word onto the image. The proposed approach is robust to different kinds of text appearances, including font size, font style, color, and background. Combining the respective strengths of different complementary techniques and overcoming their shortcomings, the proposed method uses efficient character detection and localization technique and multiclass classifier to recognize the text accurately. The proposed approach successfully recognizes text on natural scene images and does not depend on a particular alphabet, text background. It works with a wide variety in size of characters and can handle up to 20 degree skewness efficiently.